Client Report - What’s in a Name?

Table of contents

• Project Notes
• Source code
• QUESTION|TASK 1
• QUESTION|TASK 2
• QUESTION|TASK 3
• QUESTION|TASK 4
• STRETCH QUESTION|TASK 1

import pandas as pd
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

Project Notes

For Project 1 the answer to each question should include a chart and a written response. The years labels on your charts should not include a comma. At least two of your charts must include reference marks.

import os

os.getcwd()
from p1_source import my_name_plot_data

# my_name_plot_data

Source code

source code available at p1_source.py

QUESTION|TASK 1

How does your name at your birth year compare to its use historically?

my name, “Dallin” occured most about 3 years after I was born, but the name had been trending up from obscurity for almost 8 years before my birth in 1996.

from p1_source import my_name_plot

my_name_plot

QUESTION|TASK 2

If you talked to someone named Brittany on the phone, what is your guess of his or her age? What ages would you not guess?

The dataset indicates that Britanys of age 35 (in 2025) are most common.There are very few people by that name older than 50, and only a few younger than 19.

from p1_source import brittany_plot

brittany_plot

QUESTION|TASK 3

Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names in a single chart. What trends do you notice?

all names have a dip starting after about 1925, and a massive boom during the war years, with a peak after the end of the war.

from p1_source import q3_data_plot
q3_data_plot

this graph attempts to normalize the data relative to their frequency in 1920. they seem to follow a really similar pattern, but with peter being the most strongly affected

from p1_source import q3_data_plot_alt
q3_data_plot_alt

QUESTION|TASK 4

Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage?

Neo from the Matrix (1999) has no occurances before 1999. This corrolation might indicate a causal relationship between the release of the movie and the occurance of the name. There seems to be no signficant decrease in the name’s frequency in the 1.5 decades of data since the movie’s release. Maybe matrix sequel movies had an effect. But both of the two sequels were released in ’03 (excepting the 2021 reboot or whatever it was). The name seems relatively uncommon but I didn’t do a comparison to aggregate frequencies for other names in the set

# Include and execute your code here
from p1_source import movie_name_plot, movie_name_data

movie_name_plot

movie_name_data

	name	year	Total
287684	Neo	1999	7.0
287685	Neo	2000	50.0
287686	Neo	2001	64.0
287687	Neo	2002	25.0
287688	Neo	2003	51.0
287689	Neo	2004	67.0
287690	Neo	2005	46.0
287691	Neo	2006	70.0
287692	Neo	2007	43.0
287693	Neo	2008	30.0
287694	Neo	2009	46.0
287695	Neo	2010	30.0
287696	Neo	2011	28.0
287697	Neo	2012	29.0
287698	Neo	2013	26.0
287699	Neo	2014	52.0
287700	Neo	2015	38.0

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STRETCH QUESTION|TASK 1

Reproduce the chart Elliot using the data from the names_year.csv file.

I created the chart and used some ggplot tooling to attempt to adjust to look & feel of the chart to match the example. I couldn’t figure out labels for vertical lines. the geom_vline() doesn’t seem to support labels and i was having trouble with geom_text() causing an entire render failure

# Include and execute your code here
from p1_source import elliot_data, elliot_plot

elliot_data

	name	year	Total
118687	Elliot	1911.0	5.0
118688	Elliot	1912.0	6.0
118689	Elliot	1913.0	11.0
118690	Elliot	1914.0	24.0
118691	Elliot	1915.0	22.0
...	...	...	...
118787	Elliot	2011.0	891.5
118788	Elliot	2012.0	1042.5
118789	Elliot	2013.0	1064.5
118790	Elliot	2014.0	1199.0
118791	Elliot	2015.0	1250.0

105 rows × 3 columns

elliot_plot

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